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This technology includes a generated polyclonal antibody in rabbit that detects the mitochondrial uniporter (MCU) protein. This antibody was created by immunizing rabbits with a synthesized sequence of the MCU protein and can be used to identify and quantify MCU protein in various tissues. The polyclonal nature of the antibody ensures it recognizes multiple epitopes on the MCU, enhancing detection reliability. This technology is crucial for understanding MCU's role in mitochondrial function and mammalian physiology.

This technology includes antibodies for TMC1 protein as a treatment for hearing loss. TMC1 is one of the common genes causing hereditary hearing loss. Our laboratory used synthetic peptides corresponding to the TMC1 protein to immunize rabbits. The resulting antisera were shown to bind to TMC1 protein expressed in heterologous expression systems. TMC1 protein is required for the transduction of sound into electrical impulses in inner ear sensory cells.

This technology includes a fully automatic 3D image processing system to segment the heart as well as other organs from contrast-enhanced cardiac computed tomography (CCT) images. Our method detects four cardiac chambers including left ventricle, right ventricle, left atrium, right atrium, as well as the ascending aorta and left ventricular myocardium. It also classifies noncardiac tissue structures in the CCT images such as lung, chest wall, spine, descending aorta, and liver.

This technology includes a small molecule drug (VDAC inhibitor, also known as VBIT4) that may be useful for inhibiting lupus disease. To test lupus animal model, VBIT4 was continuously administered for 5 weeks to mice and there was no mortality or clinical symptoms in these animals. Additionally, VBIT4 treatment blocked the development of skin lesions and alopecia of the ears and face, and suppressed the thickening of the epidermis that accompanies leukocyte infiltration.

This technology includes rhesus macaque induced pluripotent stem cells (iPSCs) lines from multiple animals and various types of cells to establish this pre-clinical model. iPSCs are a type of pluripotent stem cell that can be generated from adult somatic cells. The iPSC technology holds great potential for regenerative medicine. Before clinical application, it is critical to evaluate safety and efficacy in a clinically-relevant animal model. We propose that non-human primate models are particularly relevant to test iPSC-based cell therapies.

The National Institute on Drug Abuse (NIDA) is seeking interested parties to license or co-develop GDNFOS peptides and non-coding RNAs as therapeutic agents for neurodegenerative diseases.

An abstract for this invention was published in the Federal Register on June 10, 2022. The family of coronaviruses cause upper respiratory tract disease in humans and have caused three major disease outbreaks in recent history: the 2003 SARS outbreak, the 2012 MERS outbreak, and the current SARS-CoV-2 pandemic. There is an urgent need for strategies that broadly target coronaviruses, both to deal with new SARS-CoV-2 variants and future coronavirus outbreaks.

Researchers at the National Cancer Institute (NCI) have developed orthotopic allograft models for pancreatic cancer that utilize low passage primary pancreatic adenocarcinoma cells or tumor fragments implanted into the cancer-free pancreata of recipient syngeneic immunocompetent mice. Tumor development in these models is more synchronized, latency is substantially shortened, and tumors develop only in one location, as pre-determined by the choice of a site for cells/tumor fragment implantation.

249 million people were afflicted with malaria in 2022. There are five Plasmodium parasite species that cause malaria in humans. Of the five, Plasmodium falciparum causes most of the incidence of human disease. Most advanced malaria vaccine candidates can confer only partial, short-term protection in malaria-endemic areas. The pathogenesis of malaria is associated with blood-stage infection and antibodies specific to the parasite blood-stage antigens may be able to control parasitemia.

This technology includes a novel method to train deep learning convolution neural network model to improve the signal-noise-ratio for the magnetic resonance (MR) imaging. The novelty lies on the fact that actual MR imaging physics information is used in the deep learning training. The resulting model achieves significant signal-to-noise ratio (SNR) improved for different acceleration factors in MR imaging. The resulting model can be used for many body anatomies (e.g., brain, heart, liver, spine, etc.) to significantly improve the SNR.